Comparative Analysis of Machine learning models for Predicting Dam overflow and Structural Risks in Ethiopia

Dam safety is a critical concern in countries with growing infrastructure and increasing climate variability. Ethiopia has several large dams that support hydropower generation, irrigation, and water management, but these structures face risks related to extreme rainfall, hydrological variability, and structural stress. This technical report presents a comparative analysis of machine learning models for predicting dam overflow and structural risk conditions in Ethiopia. The study evaluates multiple machine learning algorithms to determine their effectiveness in predicting potential overflow events and structural risk indicators using historical hydrological and environmental data. The models are assessed based on prediction accuracy, robustness, and suitability for early warning systems. By identifying the most reliable predictive approaches, this research aims to support improved dam monitoring, risk management, and disaster prevention strategies. The findings may assist engineers, policymakers, and researchers in developing data-driven decision-making tools for dam safety and water resource management in Ethiopia and similar regions. This work contributes to the growing field of machine learning applications in infrastructure safety and environmental risk prediction.